Deep Learning Toolbox Model for ResNet-101 Network
Pretrained Resnet-101 network model for image classification
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Actualizado
11 sep 2024
ResNet-101 is a pretrained model that has been trained on a subset of the ImageNet database. The model is trained on more than a million images, has 347 layers in total, corresponding to a 101 layer residual network, and can classify images into 1000 object categories (e.g. keyboard, mouse, pencil, and many animals).
Opening the resnet101.mlpkginstall file from your operating system or from within MATLAB will initiate the installation process for the release you have.
This mlpkginstall file is functional for R2017b and beyond. Use resnet101 instead of imagePretrainedNetwork if using a release prior to R2024a.
Usage Example:
% Access the trained model
[net, classes] = imagePretrainedNetwork("resnet101");
% See details of the architecture
net.Layers
% Read the image to classify
I = imread('peppers.png');
% Adjust size of the image
sz = net.Layers(1).InputSize
I = I(1:sz(1),1:sz(2),1:sz(3));
% Classify the image using ResNet-101
scores = predict(net, single(I));
label = scores2label(scores, classes)
% Show the image and the classification results
figure
imshow(I)
text(10,20,char(label),'Color','white')
Compatibilidad con la versión de MATLAB
Se creó con
R2017b
Compatible con cualquier versión desde R2017b hasta R2024b
Compatibilidad con las plataformas
Windows macOS (Apple Silicon) macOS (Intel) LinuxCategorías
Más información sobre Deep Learning Toolbox en Help Center y MATLAB Answers.
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Inspiración para: Pre-trained 3D ResNet-101
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